Intro_to_PO_Tutorials/Thermal_wind.ipynb

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    "# Part 2: Thermal Wind\n",
    "Andrew Delman, updated 2024-04-04.\n",
    "\n",
    "## Objectives\n",
    "\n",
    "To use ECCO state estimate output to explore thermal wind and its use in understanding the ocean circulation.\n",
    "\n",
    "By the end of the tutorial, you will be able to:\n",
    "\n",
    "* Plot transects along lines of latitude in the ECCO native grid\n",
    "* Rotate fields in model axes to true zonal/meridional orientation\n",
    "* Relate the density structure to the direction of oceanic flow (thermal wind balance)\n",
    "* Estimate oceanic velocity using thermal wind and assumption of a level of no motion\n",
    "\n",
    "## Introduction\n",
    "\n",
    "In the previous tutorial we established that geostrophic balance explains most of the oceanic circulation--at least at the spatial and temporal scales of ECCOv4r4 output. Now we consider thermal wind, a property of geostrophic flow that has been very useful to atmospheric and ocean scientists alike. Thermal wind balance is derived directly from geostrophic balance (in horizontal directions)\n",
    "\n",
    "$$\n",
    "v_g = \\frac{1}{f\\rho}\\frac{\\partial{p}}{\\partial{x}}\n",
    "$$\n",
    "$$\n",
    "u_g = -\\frac{1}{f\\rho}\\frac{\\partial{p}}{\\partial{y}}\n",
    "$$\n",
    "\n",
    "and hydrostatic balance (in the vertical direction)\n",
    "\n",
    "$$\n",
    "\\frac{\\partial{p}}{\\partial{z}} = -{\\rho}g\n",
    "$$\n",
    "\n",
    "Therefore by differentiating geostrophic balance along the $z$ axis and assuming hydrostatic conditions, we get thermal wind balance:\n",
    "\n",
    "$$\n",
    "\\frac{\\partial{v_g}}{\\partial{z}} = -\\frac{g}{f\\rho}\\frac{\\partial{\\rho}}{\\partial{x}}\n",
    "$$\n",
    "$$\n",
    "\\frac{\\partial{u_g}}{\\partial{z}} = \\frac{g}{f\\rho}\\frac{\\partial{\\rho}}{\\partial{y}}\n",
    "$$\n",
    "\n",
    "(cf. Vallis ch. 2.8, Kundu and Cohen ch. 14.5, Gill ch. 7.7)\n",
    "\n",
    "The *thermal wind* name originates from the application of these equations to atmospheric dynamics, where the velocities are the wind and density is mostly a function of temperature. But the balance is just as important to oceanic dynamics. In both the atmosphere and ocean, thermal wind enabled early measurements of temperature (and salinity in the ocean) to be used to construct velocity fields, where velocities are often difficult to measure directly. And even in present times when velocities can be measured directly, the thermal wind relation connects changes in heat and salt content to changes in the ocean circulation.\n",
    "\n",
    "## Viewing and Plotting Density\n",
    "\n",
    "### Transect on a single tile\n",
    "\n",
    "Let's use the density and velocity output datasets from ECCOv4r4 for January 2000, as used in the previous tutorial. This time, we'll focus on ```tile=10```, which is rotated counterclockwise so that the $j$ index increases with increasing longitude (not latitude), and the $i$ index increases with *decreasing* latitude. Tiles 7-12 (the 8th through 13th tiles, remember Python indexing starts at 0) are rotated in this way, as illustrated in the following tile plot\n",
    "\n",
    "![llc90 tile layout](../figures/llc90_0.png)\n",
    "\n",
    "For this tutorial, we will need the ECCO datasets with the following ShortNames:\n",
    "\n",
    "- **ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4** (Jan 2000)\n",
    "- **ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4** (Jan 2000)\n",
    "- **ECCO_L4_GEOMETRY_LLC0090GRID_V4R4** (no time dimension; can use any time 1992-2017 in download functions)\n",
    "\n",
    "To download these datasets to your local machine, you can use the [ecco_download.py](https://raw.githubusercontent.com/ECCO-GROUP/ECCO-v4-Python-Tutorial/master/ECCO-ACCESS/ecco_download.py) module. See the [previous tutorial](https://ecco-v4-python-tutorial.readthedocs.io/Geostrophic_balance.html#Download-the-ECCO-output) or the [ECCO download](https://ecco-v4-python-tutorial.readthedocs.io/Downloading_ECCO_Datasets_from_PODAAC_Python.html) for more info on how to use this module. If you are working on an AWS instance, just set `incloud_access = True` in the 2nd cell below and the datasets will be downloaded or opened remotely using the `ecco_s3_retrieve.py` module.\n",
    "\n",
    "We are going to load the density dataset and plot density on longitude-depth axes following the line in the grid nearest to $26^{o}$ N, i.e., ```i = 73```."
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    "# first import needed packages\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import xmitgcm\n",
    "import xgcm\n",
    "import glob\n",
    "from os.path import expanduser,join\n",
    "import sys\n",
    "user_home_dir = expanduser('~')\n",
    "sys.path.append(join(user_home_dir,'ECCOv4-py'))   # only needed if ecco_v4_py files are stored under this directory\n",
    "import matplotlib.pyplot as plt\n",
    "import ecco_v4_py as ecco\n",
    "from ecco_po_tutorials import *   # import from ecco_po_tutorials.py module downloaded in the last tutorial\n",
    "\n",
    "# ShortNames\n",
    "vel_monthly_shortname = \"ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4\"\n",
    "denspress_monthly_shortname = \"ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4\"\n",
    "grid_params_shortname = \"ECCO_L4_GEOMETRY_LLC0090GRID_V4R4\""
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    "# if working in the AWS cloud, set incloud_access = True and run this cell\n",
    "\n",
    "incloud_access = False\n",
    "\n",
    "ShortNames_list = [vel_monthly_shortname,denspress_monthly_shortname,grid_params_shortname]\n",
    "if incloud_access == True:\n",
    "    from ecco_s3_retrieve import ecco_podaac_s3_get_diskaware\n",
    "    # download files to default path ~/Downloads/ECCO_V4r4_PODAAC/\n",
    "    files_dict = ecco_podaac_s3_get_diskaware(ShortNames=ShortNames_list,\\\n",
    "                                              StartDate='2000-01',EndDate='2000-01',\\\n",
    "                                              max_avail_frac=0.5,\\\n",
    "                                              download_root_dir=None)"
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       "    Z_bnds     (k, nv) float32 400B ...\n",
       "Dimensions without coordinates: nv, nb\n",
       "Data variables:\n",
       "    RHOAnoma   (time, k, tile, j, i) float32 21MB ...\n",
       "    DRHODR     (time, k_l, tile, j, i) float32 21MB ...\n",
       "    PHIHYD     (time, k, tile, j, i) float32 21MB ...\n",
       "    PHIHYDcR   (time, k, tile, j, i) float32 21MB ...\n",
       "Attributes: (12/62)\n",
       "    acknowledgement:                 This research was carried out by the Jet...\n",
       "    author:                          Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                   Grid\n",
       "    comment:                         Fields provided on the curvilinear lat-l...\n",
       "    Conventions:                     CF-1.8, ACDD-1.3\n",
       "    coordinates_comment:             Note: the global &#x27;coordinates&#x27; attribute...\n",
       "    ...                              ...\n",
       "    time_coverage_duration:          P1M\n",
       "    time_coverage_end:               2000-02-01T00:00:00\n",
       "    time_coverage_resolution:        P1M\n",
       "    time_coverage_start:             2000-01-01T00:00:00\n",
       "    title:                           ECCO Ocean Density, Stratification, and ...\n",
       "    uuid:                            166a1992-4182-11eb-82f9-0cc47a3f43f9</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-51b79bbd-b3bb-4146-9459-469b9b5117fa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-51b79bbd-b3bb-4146-9459-469b9b5117fa' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>i</span>: 90</li><li><span class='xr-has-index'>i_g</span>: 90</li><li><span class='xr-has-index'>j</span>: 90</li><li><span class='xr-has-index'>j_g</span>: 90</li><li><span class='xr-has-index'>k</span>: 50</li><li><span class='xr-has-index'>k_u</span>: 50</li><li><span class='xr-has-index'>k_l</span>: 50</li><li><span class='xr-has-index'>k_p1</span>: 51</li><li><span class='xr-has-index'>tile</span>: 13</li><li><span class='xr-has-index'>time</span>: 1</li><li><span>nv</span>: 2</li><li><span>nb</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-23650e35-10a1-4c07-826f-65ef1dd92c60' class='xr-section-summary-in' type='checkbox'  checked><label for='section-23650e35-10a1-4c07-826f-65ef1dd92c60' class='xr-section-summary' >Coordinates: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-08f30a0d-e6fc-4aff-aac8-988d7f2f2fe2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08f30a0d-e6fc-4aff-aac8-988d7f2f2fe2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1592855-409e-4be7-b4b3-26d7cb895655' class='xr-var-data-in' type='checkbox'><label for='data-e1592855-409e-4be7-b4b3-26d7cb895655' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at tracer and &#x27;v&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>XC</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, tracer (e.g., THETA) and &#x27;v&#x27; variables (e.g., VVEL) have the same x coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i_g</span></div><div class='xr-var-dims'>(i_g)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-8baaa588-bc71-4755-b72d-6ca9e009d8c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8baaa588-bc71-4755-b72d-6ca9e009d8c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89d84a7e-ce1d-443b-8d60-942f8400f771' class='xr-var-data-in' type='checkbox'><label for='data-89d84a7e-ce1d-443b-8d60-942f8400f771' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at &#x27;u&#x27; and &#x27;g&#x27; locations</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd><dt><span>swap_dim :</span></dt><dd>XG</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, &#x27;u&#x27; (e.g., UVEL) and &#x27;g&#x27; variables (e.g., XG) have the same x coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j</span></div><div class='xr-var-dims'>(j)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-3b2054df-d980-4cb7-a9f9-54d4fc17d53e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b2054df-d980-4cb7-a9f9-54d4fc17d53e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb78ba77-cf13-4bae-9e26-060eaf7d9dad' class='xr-var-data-in' type='checkbox'><label for='data-cb78ba77-cf13-4bae-9e26-060eaf7d9dad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>grid index in y for variables at tracer and &#x27;u&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>YC</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, tracer (e.g., THETA) and &#x27;u&#x27; variables (e.g., UVEL) have the same y coordinate on the model grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j_g</span></div><div class='xr-var-dims'>(j_g)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-084ff7fe-c33b-4856-9c40-cfae8dbd4139' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-084ff7fe-c33b-4856-9c40-cfae8dbd4139' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ce1ce02-c723-457e-b254-ecf1672af181' class='xr-var-data-in' type='checkbox'><label for='data-7ce1ce02-c723-457e-b254-ecf1672af181' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>grid index in y for variables at &#x27;v&#x27; and &#x27;g&#x27; locations</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd><dt><span>swap_dim :</span></dt><dd>YG</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, &#x27;v&#x27; (e.g., VVEL) and &#x27;g&#x27; variables (e.g., XG) have the same y coordinate.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 44 45 46 47 48 49</div><input id='attrs-579fb81a-a43a-4e7f-8120-d0702a3cd076' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-579fb81a-a43a-4e7f-8120-d0702a3cd076' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ffa55f5-b8e5-4dfc-aa53-ca81f86c3aaa' class='xr-var-data-in' type='checkbox'><label for='data-6ffa55f5-b8e5-4dfc-aa53-ca81f86c3aaa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>grid index in z for tracer variables</dd><dt><span>swap_dim :</span></dt><dd>Z</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k_u</span></div><div class='xr-var-dims'>(k_u)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 44 45 46 47 48 49</div><input id='attrs-6b13e6af-ca12-40b2-b35b-cabad0d2b97b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b13e6af-ca12-40b2-b35b-cabad0d2b97b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2606d67-49eb-443d-89ed-8a20a1fef04b' class='xr-var-data-in' type='checkbox'><label for='data-d2606d67-49eb-443d-89ed-8a20a1fef04b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>c_grid_axis_shift :</span></dt><dd>0.5</dd><dt><span>swap_dim :</span></dt><dd>Zu</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in z corresponding to the bottom face of tracer grid cells (&#x27;w&#x27; locations)</dd><dt><span>comment :</span></dt><dd>First index corresponds to the bottom surface of the uppermost tracer grid cell. The use of &#x27;u&#x27; in the variable name follows the MITgcm convention for ocean variables in which the upper (u) face of a tracer grid cell on the logical grid corresponds to the bottom face of the grid cell on the physical grid.</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k_l</span></div><div class='xr-var-dims'>(k_l)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 44 45 46 47 48 49</div><input id='attrs-b50ebbfe-faf0-4f09-b518-58116da8c3c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b50ebbfe-faf0-4f09-b518-58116da8c3c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae7f4cc5-8975-4746-9f1d-5297ed3cb8e5' class='xr-var-data-in' type='checkbox'><label for='data-ae7f4cc5-8975-4746-9f1d-5297ed3cb8e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd><dt><span>swap_dim :</span></dt><dd>Zl</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in z corresponding to the top face of tracer grid cells (&#x27;w&#x27; locations)</dd><dt><span>comment :</span></dt><dd>First index corresponds to the top surface of the uppermost tracer grid cell. The use of &#x27;l&#x27; in the variable name follows the MITgcm convention for ocean variables in which the lower (l) face of a tracer grid cell on the logical grid corresponds to the top face of the grid cell on the physical grid.</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k_p1</span></div><div class='xr-var-dims'>(k_p1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 45 46 47 48 49 50</div><input id='attrs-ff481151-c08d-4979-abe4-394a811b8ca5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff481151-c08d-4979-abe4-394a811b8ca5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69b98149-2ef8-4aa1-954d-2e2465b6bb3e' class='xr-var-data-in' type='checkbox'><label for='data-69b98149-2ef8-4aa1-954d-2e2465b6bb3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>grid index in z for variables at &#x27;w&#x27; locations</dd><dt><span>c_grid_axis_shift :</span></dt><dd>[-0.5  0.5]</dd><dt><span>swap_dim :</span></dt><dd>Zp1</dd><dt><span>comment :</span></dt><dd>Includes top of uppermost model tracer cell (k_p1=0) and bottom of lowermost tracer cell (k_p1=51).</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>tile</span></div><div class='xr-var-dims'>(tile)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-1fd5909b-78ba-484a-9997-ccf69e8c2d4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1fd5909b-78ba-484a-9997-ccf69e8c2d4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ec3ae2f-74d4-49a2-a027-449d32ab3735' class='xr-var-data-in' type='checkbox'><label for='data-1ec3ae2f-74d4-49a2-a027-449d32ab3735' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>lat-lon-cap tile index</dd><dt><span>comment :</span></dt><dd>The ECCO V4 horizontal model grid is divided into 13 tiles of 90x90 cells for convenience.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-16T12:00:00</div><input id='attrs-d5aebf16-3385-4c27-bc00-d690fe5f2d3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5aebf16-3385-4c27-bc00-d690fe5f2d3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6324cc97-e199-4d32-be4d-27cb82a43083' class='xr-var-data-in' type='checkbox'><label for='data-6324cc97-e199-4d32-be4d-27cb82a43083' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>center time of averaging period</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-661f6d9f-d2a0-4ea2-9b6a-0bbe428ea4cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-661f6d9f-d2a0-4ea2-9b6a-0bbe428ea4cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac2821dd-fc3a-48d1-a8f3-1627a862ba54' class='xr-var-data-in' type='checkbox'><label for='data-ac2821dd-fc3a-48d1-a8f3-1627a862ba54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of tracer grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><dt><span>bounds :</span></dt><dd>XC_bnds</dd><dt><span>comment :</span></dt><dd>nonuniform grid spacing</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>[105300 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3fd8822e-5792-4a4b-b960-971c2d26f51f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fd8822e-5792-4a4b-b960-971c2d26f51f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16b078dd-0041-43f6-a8b0-b7ca3cae2503' class='xr-var-data-in' type='checkbox'><label for='data-16b078dd-0041-43f6-a8b0-b7ca3cae2503' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of tracer grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><dt><span>bounds :</span></dt><dd>YC_bnds</dd><dt><span>comment :</span></dt><dd>nonuniform grid spacing</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>[105300 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XG</span></div><div class='xr-var-dims'>(tile, j_g, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-48db1b67-a735-4448-8530-df6c86566b73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48db1b67-a735-4448-8530-df6c86566b73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e93fd23-bfad-40e1-abe1-5c952ccad5ab' class='xr-var-data-in' type='checkbox'><label for='data-6e93fd23-bfad-40e1-abe1-5c952ccad5ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of &#x27;southwest&#x27; corner of tracer grid cell</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd><dt><span>comment :</span></dt><dd>Nonuniform grid spacing. Note: &#x27;southwest&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>[105300 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YG</span></div><div class='xr-var-dims'>(tile, j_g, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5256e3ac-b4e3-4469-b4c3-7af7e593b658' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5256e3ac-b4e3-4469-b4c3-7af7e593b658' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a9ad418-52fb-4b21-b8f5-aa2ef5656e86' class='xr-var-data-in' type='checkbox'><label for='data-5a9ad418-52fb-4b21-b8f5-aa2ef5656e86' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of &#x27;southwest&#x27; corner of tracer grid cell</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd><dt><span>comment :</span></dt><dd>Nonuniform grid spacing. Note: &#x27;southwest&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>[105300 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Z</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-41334b18-e38b-4847-8b43-74fa35f92994' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41334b18-e38b-4847-8b43-74fa35f92994' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cd2e265-ac2b-4afa-963f-df324504b58b' class='xr-var-data-in' type='checkbox'><label for='data-5cd2e265-ac2b-4afa-963f-df324504b58b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth of tracer grid cell center</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>bounds :</span></dt><dd>Z_bnds</dd><dt><span>comment :</span></dt><dd>Non-uniform vertical spacing.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>[50 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Zp1</span></div><div class='xr-var-dims'>(k_p1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-928bf8c5-de53-4035-a311-0e58ce770db3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-928bf8c5-de53-4035-a311-0e58ce770db3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-652cf03f-dbbd-4ab9-b1ed-21ab7537a94a' class='xr-var-data-in' type='checkbox'><label for='data-652cf03f-dbbd-4ab9-b1ed-21ab7537a94a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth of tracer grid cell interface</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>comment :</span></dt><dd>Contains one element more than the number of vertical layers. First element is 0m, the depth of the upper interface of the surface grid cell. Last element is the depth of the lower interface of the deepest grid cell.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>[51 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Zu</span></div><div class='xr-var-dims'>(k_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-691c30ca-5244-4ed6-a0e9-aaf942809993' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-691c30ca-5244-4ed6-a0e9-aaf942809993' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93b17c9e-7fee-41da-bbab-034d10662193' class='xr-var-data-in' type='checkbox'><label for='data-93b17c9e-7fee-41da-bbab-034d10662193' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>long_name :</span></dt><dd>depth of the bottom face of tracer grid cells</dd><dt><span>comment :</span></dt><dd>First element is -10m, the depth of the bottom face of the first tracer grid cell. Last element is the depth of the bottom face of the deepest grid cell. The use of &#x27;u&#x27; in the variable name follows the MITgcm convention for ocean variables in which the upper (u) face of a tracer grid cell on the logical grid corresponds to the bottom face of the grid cell on the physical grid. In other words, the logical vertical grid of MITgcm ocean variables is inverted relative to the physical vertical grid.</dd></dl></div><div class='xr-var-data'><pre>[50 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Zl</span></div><div class='xr-var-dims'>(k_l)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fdb11c7f-5a15-4b8d-aee8-3dd1df92f8b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdb11c7f-5a15-4b8d-aee8-3dd1df92f8b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5b38c1f-2693-47eb-888c-02493fb54fdd' class='xr-var-data-in' type='checkbox'><label for='data-a5b38c1f-2693-47eb-888c-02493fb54fdd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>long_name :</span></dt><dd>depth of the top face of tracer grid cells</dd><dt><span>comment :</span></dt><dd>First element is 0m, the depth of the top face of the first tracer grid cell (ocean surface). Last element is the depth of the top face of the deepest grid cell. The use of &#x27;l&#x27; in the variable name follows the MITgcm convention for ocean variables in which the lower (l) face of a tracer grid cell on the logical grid corresponds to the top face of the grid cell on the physical grid. In other words, the logical vertical grid of MITgcm ocean variables is inverted relative to the physical vertical grid.</dd></dl></div><div class='xr-var-data'><pre>[50 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nv)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7b6ca094-fc7d-4acc-8133-11617d8c46aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b6ca094-fc7d-4acc-8133-11617d8c46aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5680d3f7-00c0-4a36-b2f7-b15a81ff1e3a' class='xr-var-data-in' type='checkbox'><label for='data-5680d3f7-00c0-4a36-b2f7-b15a81ff1e3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Start and end times of averaging period.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>time bounds of averaging period</dd></dl></div><div class='xr-var-data'><pre>[2 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC_bnds</span></div><div class='xr-var-dims'>(tile, j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-380486d0-2fd4-4d4d-a332-532c05a76ece' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-380486d0-2fd4-4d4d-a332-532c05a76ece' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cf0abdf-74b4-4962-8b15-08b5922365f9' class='xr-var-data-in' type='checkbox'><label for='data-3cf0abdf-74b4-4962-8b15-08b5922365f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Bounds array follows CF conventions. XC_bnds[i,j,0] = &#x27;southwest&#x27; corner (j-1, i-1), XC_bnds[i,j,1] = &#x27;southeast&#x27; corner (j-1, i+1), XC_bnds[i,j,2] = &#x27;northeast&#x27; corner (j+1, i+1), XC_bnds[i,j,3]  = &#x27;northwest&#x27; corner (j+1, i-1). Note: &#x27;southwest&#x27;, &#x27;southeast&#x27;, northwest&#x27;, and &#x27;northeast&#x27; do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitudes of tracer grid cell corners</dd></dl></div><div class='xr-var-data'><pre>[421200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YC_bnds</span></div><div class='xr-var-dims'>(tile, j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-24efa4a2-356c-434f-88b8-0bb257c88df1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24efa4a2-356c-434f-88b8-0bb257c88df1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-048201c4-6cf8-4523-9652-b25124d7b2a7' class='xr-var-data-in' type='checkbox'><label for='data-048201c4-6cf8-4523-9652-b25124d7b2a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Bounds array follows CF conventions. YC_bnds[i,j,0] = &#x27;southwest&#x27; corner (j-1, i-1), YC_bnds[i,j,1] = &#x27;southeast&#x27; corner (j-1, i+1), YC_bnds[i,j,2] = &#x27;northeast&#x27; corner (j+1, i+1), YC_bnds[i,j,3]  = &#x27;northwest&#x27; corner (j+1, i-1). Note: &#x27;southwest&#x27;, &#x27;southeast&#x27;, northwest&#x27;, and &#x27;northeast&#x27; do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitudes of tracer grid cell corners</dd></dl></div><div class='xr-var-data'><pre>[421200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Z_bnds</span></div><div class='xr-var-dims'>(k, nv)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6e0b2ae9-accf-4c41-a9e6-b37c94e336a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e0b2ae9-accf-4c41-a9e6-b37c94e336a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ededa6de-efd6-4cb8-ab59-2baa82612629' class='xr-var-data-in' type='checkbox'><label for='data-ededa6de-efd6-4cb8-ab59-2baa82612629' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>One pair of depths for each vertical level.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>depths of tracer grid cell upper and lower interfaces</dd></dl></div><div class='xr-var-data'><pre>[100 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2f264f7f-adbc-4ccd-ae53-413b4d21a3bb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2f264f7f-adbc-4ccd-ae53-413b4d21a3bb' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>RHOAnoma</span></div><div class='xr-var-dims'>(time, k, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a138b59a-07d1-4bbf-8b15-1b4872b7be62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a138b59a-07d1-4bbf-8b15-1b4872b7be62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c3a66b0-8997-41fe-b773-4e414e183e5e' class='xr-var-data-in' type='checkbox'><label for='data-3c3a66b0-8997-41fe-b773-4e414e183e5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>In-situ seawater density anomaly</dd><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>valid_min :</span></dt><dd>-18.81316375732422</dd><dt><span>valid_max :</span></dt><dd>25.540061950683594</dd><dt><span>comment :</span></dt><dd>In-situ seawater density anomaly relative to the reference density, rhoConst. rhoConst = 1029 kg m-3</dd></dl></div><div class='xr-var-data'><pre>[5265000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DRHODR</span></div><div class='xr-var-dims'>(time, k_l, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e32c7c69-ce0c-45c6-b998-0e5744924bda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e32c7c69-ce0c-45c6-b998-0e5744924bda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31565033-7120-49c4-b6fb-cc0631148485' class='xr-var-data-in' type='checkbox'><label for='data-31565033-7120-49c4-b6fb-cc0631148485' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Density stratification</dd><dt><span>units :</span></dt><dd>kg m-3 m-1</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>Density stratification: d(sigma) d z-1. Note: density computations are done with in-situ density. The vertical derivatives of in-situ density and locally-referenced potential density are identical  The equation of state is a modified UNESCO formula by Jackett and McDougall (1995), which uses the model variable potential temperature as input assuming a horizontally and temporally constant pressure of $p_0=-g \\rho_{0} z$.</dd><dt><span>valid_min :</span></dt><dd>-0.7393171787261963</dd><dt><span>valid_max :</span></dt><dd>3.208351699868217e-05</dd></dl></div><div class='xr-var-data'><pre>[5265000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHIHYD</span></div><div class='xr-var-dims'>(time, k, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fa44b038-4717-4da6-923d-011da401ade6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa44b038-4717-4da6-923d-011da401ade6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c468c41-03f9-4804-a520-c8a1d1116bb6' class='xr-var-data-in' type='checkbox'><label for='data-5c468c41-03f9-4804-a520-c8a1d1116bb6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Ocean hydrostatic pressure anomaly</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>PHIHYD = p(k) / rhoConst - g z*(k,t), where p = hydrostatic ocean pressure at depth level k, rhoConst = reference density (1029 kg m-3), g is acceleration due to gravity (9.81 m s-2), and z*(k,t) is model depth at level k and time t. Units: p:[kg m-1 s-2], rhoConst:[kg m-3], g:[m s-2], H(t):[m]. Note: includes atmospheric pressure loading. Quantity referred to in some contexts as hydrostatic pressure anomaly. PHIBOT accounts for the model&#x27;s time-varying grid cell thickness (z* coordinate system). See PHIHYDcR for hydrostatic pressure potential anomaly calculated using time-invariant grid cell thicknesses. PHIHYD is NOT corrected for global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH). </dd><dt><span>valid_min :</span></dt><dd>79.53374481201172</dd><dt><span>valid_max :</span></dt><dd>783.2001953125</dd></dl></div><div class='xr-var-data'><pre>[5265000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHIHYDcR</span></div><div class='xr-var-dims'>(time, k, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cc1c62ba-999c-4e92-a18f-a7cb2355afc2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc1c62ba-999c-4e92-a18f-a7cb2355afc2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-766aff82-1d75-43f2-9d34-17407fdbca92' class='xr-var-data-in' type='checkbox'><label for='data-766aff82-1d75-43f2-9d34-17407fdbca92' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Ocean hydrostatic pressure anomaly at constant depths</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>PHIHYD = p(k) / rhoConst - g z(k,t), where p = hydrostatic ocean pressure at depth level k, rhoConst = reference density (1029 kg m-3), g is acceleration due to gravity (9.81 m s-2), and z(k,t) is fixed model depth at level k. Units: p:[kg m-1 s-2], rhoConst:[kg m-3], g:[m s-2], H(t):[m]. Note: includes atmospheric pressure loading. Quantity referred to in some contexts as hydrostatic pressure potential anomaly. PHIHYDcR is calculated with respect to the model&#x27;s initial, time-invariant grid cell thicknesses. See PHIHYD for hydrostatic pressure anomaly calculated using model&#x27;s time-variable grid cell thicknesses (z* coordinate system). PHIHYDcR is is NOT corrected for global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH). </dd><dt><span>valid_min :</span></dt><dd>78.17041778564453</dd><dt><span>valid_max :</span></dt><dd>783.6217041015625</dd></dl></div><div class='xr-var-data'><pre>[5265000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c2d91d0-8fb7-485b-b20c-d4b5702ecffa' class='xr-section-summary-in' type='checkbox'  ><label for='section-4c2d91d0-8fb7-485b-b20c-d4b5702ecffa' class='xr-section-summary' >Indexes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>i</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e19bf0d1-5cdd-4284-98b7-b48f1d9d488b' class='xr-index-data-in' type='checkbox'/><label for='index-e19bf0d1-5cdd-4284-98b7-b48f1d9d488b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;i&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>i_g</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-66f7829c-fb31-4a7e-864d-1d53439aa102' class='xr-index-data-in' type='checkbox'/><label for='index-66f7829c-fb31-4a7e-864d-1d53439aa102' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;i_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-37583e6c-aba4-40e1-b3ef-93197d48d4bc' class='xr-index-data-in' type='checkbox'/><label for='index-37583e6c-aba4-40e1-b3ef-93197d48d4bc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;j&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j_g</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-aa046164-bc5f-4b65-8d8b-58c755d0dcd3' class='xr-index-data-in' type='checkbox'/><label for='index-aa046164-bc5f-4b65-8d8b-58c755d0dcd3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;j_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5b283f5a-0e32-42e2-954a-96da996c6653' class='xr-index-data-in' type='checkbox'/><label for='index-5b283f5a-0e32-42e2-954a-96da996c6653' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k_u</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fe07a862-26e7-4532-ba94-ebb0c512977b' class='xr-index-data-in' type='checkbox'/><label for='index-fe07a862-26e7-4532-ba94-ebb0c512977b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k_u&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k_l</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0df35667-6392-4ab1-8b6e-0fa5e71557db' class='xr-index-data-in' type='checkbox'/><label for='index-0df35667-6392-4ab1-8b6e-0fa5e71557db' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k_l&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k_p1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9ffce12d-a3f6-4b72-834b-e168b93b3af8' class='xr-index-data-in' type='checkbox'/><label for='index-9ffce12d-a3f6-4b72-834b-e168b93b3af8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k_p1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>tile</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b29e706b-671b-4492-b1bc-b69345e26185' class='xr-index-data-in' type='checkbox'/><label for='index-b29e706b-671b-4492-b1bc-b69345e26185' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=&#x27;int32&#x27;, name=&#x27;tile&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1b4d0103-28fd-4106-8c5d-cc57bae947d0' class='xr-index-data-in' type='checkbox'/><label for='index-1b4d0103-28fd-4106-8c5d-cc57bae947d0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2000-01-16 12:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-647f2a88-3f8a-4ee8-949d-6003308fb5ee' class='xr-section-summary-in' type='checkbox'  ><label for='section-647f2a88-3f8a-4ee8-949d-6003308fb5ee' class='xr-section-summary' >Attributes: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>acknowledgement :</span></dt><dd>This research was carried out by the Jet Propulsion Laboratory, managed by the California Institute of Technology under a contract with the National Aeronautics and Space Administration.</dd><dt><span>author :</span></dt><dd>Ian Fenty and Ou Wang</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>Fields provided on the curvilinear lat-lon-cap 90 (llc90) native grid used in the ECCO model.</dd><dt><span>Conventions :</span></dt><dd>CF-1.8, ACDD-1.3</dd><dt><span>coordinates_comment :</span></dt><dd>Note: the global &#x27;coordinates&#x27; attribute describes auxillary coordinates.</dd><dt><span>creator_email :</span></dt><dd>ecco-group@mit.edu</dd><dt><span>creator_institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL)</dd><dt><span>creator_name :</span></dt><dd>ECCO Consortium</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_url :</span></dt><dd>https://ecco-group.org</dd><dt><span>date_created :</span></dt><dd>2020-12-18T14:40:54</dd><dt><span>date_issued :</span></dt><dd>2020-12-18T14:40:54</dd><dt><span>date_metadata_modified :</span></dt><dd>2021-03-15T21:55:10</dd><dt><span>date_modified :</span></dt><dd>2021-03-15T21:55:10</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>180.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>-180.0</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_min :</span></dt><dd>-6134.5</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>up</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meter</dd><dt><span>history :</span></dt><dd>Inaugural release of an ECCO Central Estimate solution to PO.DAAC</dd><dt><span>id :</span></dt><dd>10.5067/ECL5M-ODE44</dd><dt><span>institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL)</dd><dt><span>instrument_vocabulary :</span></dt><dd>GCMD instrument keywords</dd><dt><span>keywords :</span></dt><dd>EARTH SCIENCE &gt; OCEANS &gt; OCEAN PRESSURE &gt; WATER PRESSURE, EARTH SCIENCE SERVICES &gt; MODELS &gt; EARTH SCIENCE REANALYSES/ASSIMILATION MODELS, EARTH SCIENCE &gt; OCEANS &gt; OCEAN PRESSURE, EARTH SCIENCE &gt; OCEANS &gt; SALINITY/DENSITY &gt; DENSITY, EARTH SCIENCE &gt; OCEANS &gt; SALINITY/DENSITY</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>Public Domain</dd><dt><span>metadata_link :</span></dt><dd>https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4</dd><dt><span>naming_authority :</span></dt><dd>gov.nasa.jpl</dd><dt><span>platform :</span></dt><dd>ERS-1/2, TOPEX/Poseidon, Geosat Follow-On (GFO), ENVISAT, Jason-1, Jason-2, CryoSat-2, SARAL/AltiKa, Jason-3, AVHRR, Aquarius, SSM/I, SSMIS, GRACE, DTU17MDT, Argo, WOCE, GO-SHIP, MEOP, Ice Tethered Profilers (ITP)</dd><dt><span>platform_vocabulary :</span></dt><dd>GCMD platform keywords</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_name :</span></dt><dd>OCEAN_DENS_STRAT_PRESS_mon_mean_2000-01_ECCO_V4r4_native_llc0090.nc</dd><dt><span>product_time_coverage_end :</span></dt><dd>2018-01-01T00:00:00</dd><dt><span>product_time_coverage_start :</span></dt><dd>1992-01-01T12:00:00</dd><dt><span>product_version :</span></dt><dd>Version 4, Release 4</dd><dt><span>program :</span></dt><dd>NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)</dd><dt><span>project :</span></dt><dd>Estimating the Circulation and Climate of the Ocean (ECCO)</dd><dt><span>publisher_email :</span></dt><dd>podaac@podaac.jpl.nasa.gov</dd><dt><span>publisher_institution :</span></dt><dd>PO.DAAC</dd><dt><span>publisher_name :</span></dt><dd>Physical Oceanography Distributed Active Archive Center (PO.DAAC)</dd><dt><span>publisher_type :</span></dt><dd>institution</dd><dt><span>publisher_url :</span></dt><dd>https://podaac.jpl.nasa.gov</dd><dt><span>references :</span></dt><dd>ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., &amp; Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928</dd><dt><span>source :</span></dt><dd>The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint method</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>summary :</span></dt><dd>This dataset provides monthly-averaged ocean density, stratification, and hydrostatic pressure on the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.</dd><dt><span>time_coverage_duration :</span></dt><dd>P1M</dd><dt><span>time_coverage_end :</span></dt><dd>2000-02-01T00:00:00</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1M</dd><dt><span>time_coverage_start :</span></dt><dd>2000-01-01T00:00:00</dd><dt><span>title :</span></dt><dd>ECCO Ocean Density, Stratification, and Hydrostatic Pressure - Monthly Mean llc90 Grid (Version 4 Release 4)</dd><dt><span>uuid :</span></dt><dd>166a1992-4182-11eb-82f9-0cc47a3f43f9</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 89MB\n",
       "Dimensions:    (i: 90, i_g: 90, j: 90, j_g: 90, k: 50, k_u: 50, k_l: 50,\n",
       "                k_p1: 51, tile: 13, time: 1, nv: 2, nb: 4)\n",
       "Coordinates: (12/22)\n",
       "  * i          (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * i_g        (i_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j_g        (j_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * k          (k) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "  * k_u        (k_u) int32 200B 0 1 2 3 4 5 6 7 8 ... 41 42 43 44 45 46 47 48 49\n",
       "    ...         ...\n",
       "    Zu         (k_u) float32 200B ...\n",
       "    Zl         (k_l) float32 200B ...\n",
       "    time_bnds  (time, nv) datetime64[ns] 16B ...\n",
       "    XC_bnds    (tile, j, i, nb) float32 2MB ...\n",
       "    YC_bnds    (tile, j, i, nb) float32 2MB ...\n",
       "    Z_bnds     (k, nv) float32 400B ...\n",
       "Dimensions without coordinates: nv, nb\n",
       "Data variables:\n",
       "    RHOAnoma   (time, k, tile, j, i) float32 21MB ...\n",
       "    DRHODR     (time, k_l, tile, j, i) float32 21MB ...\n",
       "    PHIHYD     (time, k, tile, j, i) float32 21MB ...\n",
       "    PHIHYDcR   (time, k, tile, j, i) float32 21MB ...\n",
       "Attributes: (12/62)\n",
       "    acknowledgement:                 This research was carried out by the Jet...\n",
       "    author:                          Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                   Grid\n",
       "    comment:                         Fields provided on the curvilinear lat-l...\n",
       "    Conventions:                     CF-1.8, ACDD-1.3\n",
       "    coordinates_comment:             Note: the global 'coordinates' attribute...\n",
       "    ...                              ...\n",
       "    time_coverage_duration:          P1M\n",
       "    time_coverage_end:               2000-02-01T00:00:00\n",
       "    time_coverage_resolution:        P1M\n",
       "    time_coverage_start:             2000-01-01T00:00:00\n",
       "    title:                           ECCO Ocean Density, Stratification, and ...\n",
       "    uuid:                            166a1992-4182-11eb-82f9-0cc47a3f43f9"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now open density and grid parameter files\n",
    "\n",
    "# density file\n",
    "download_root_dir = join(user_home_dir,'Downloads','ECCO_V4r4_PODAAC')\n",
    "download_dir = join(download_root_dir,denspress_monthly_shortname)\n",
    "if incloud_access == True:\n",
    "    curr_denspress_file = files_dict[denspress_monthly_shortname]\n",
    "else:\n",
    "    curr_denspress_file = list(glob.glob(join(download_dir,'*2000-01*.nc')))\n",
    "ds_dens = xr.open_dataset(curr_denspress_file[0])\n",
    "ds_dens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "43ceb6c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load grid parameters file\n",
    "grid_params_file = \"GRID_GEOMETRY_ECCO_V4r4_native_llc0090.nc\"\n",
    "if incloud_access == True:\n",
    "    grid_params_file_path = files_dict[grid_params_shortname]\n",
    "else:\n",
    "    grid_params_file_path = join(download_root_dir,grid_params_shortname,grid_params_file)\n",
    "ds_grid = xr.open_dataset(grid_params_file_path)\n",
    "\n",
    "# find index closest to target latitude\n",
    "lat_target = 26.\n",
    "target_i = np.argmin(np.abs(ds_grid.YC[10,70,:].values - lat_target))\n",
    "# the choice of j = 70 above is random, any j index from 0 to 89 will yield the same target_i\n",
    "target_i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "22a289b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # define two new functions (also in ecco_po_tutorials module, but shown here for clarity)\n",
    "\n",
    "# function to create 2 subplots with split depth axes\n",
    "def depth_two_subplots(horiz_coords,depth_coords,data,k_split,cmap,mask=None,fig=None,axs=None):\n",
    "    \"\"\"\n",
    "    Make 2 subplots with depth on y-axis, for shallow and deeper depths, given parameters:\n",
    "    horiz_coords: horizontal coordinate, xarray DataArray\n",
    "    depth_coords: depth_coordinate, xarray DataArray\n",
    "    data: 2-D xarray DataArray\n",
    "    k_split: k coordinate to split the plot at, integer\n",
    "    cmap: string specifying colormap to use\n",
    "    mask: 2-D boolean (land) mask, optional\n",
    "    fig: figure object, optional, default is new figure is created\n",
    "    axs: axes with two subplots oriented vertically, default is they are created\n",
    "    \"\"\"\n",
    "    \n",
    "    if (fig is None) and (axs is None):\n",
    "        fig,axs = plt.subplots(2,1,figsize=(10,10))    # subplots for different depths\n",
    "    elif (fig is None) or (axs is None):\n",
    "        print(\"Warning: Only one of fig or axs has been supplied, not both\")\n",
    "    curr_ax = axs[0]\n",
    "    curr_plot_0 = curr_ax.pcolormesh(horiz_coords,depth_coords[:k_split],\\\n",
    "                                      data.isel(k=np.arange(k_split)),cmap=cmap)                                      \n",
    "    if mask is not None:\n",
    "        curr_mask = mask.isel(k=np.arange(k_split))\n",
    "        plot_mask(horiz_coords,depth_coords[:k_split],curr_mask,ax=curr_ax,color=(0,0,0))\n",
    "    curr_ax.set_ylim(curr_ax.get_ylim()[::-1])\n",
    "    curr_ax.set_ylabel('Depth [m]')    \n",
    "    clim_0 = curr_plot_0.get_clim()\n",
    "    \n",
    "    curr_ax = axs[1]\n",
    "    curr_plot_1 = curr_ax.pcolormesh(horiz_coords,depth_coords[k_split:],\\\n",
    "                                      data.isel(k=np.arange(25,len(depth_coords))),cmap=cmap)                                      \n",
    "    if mask is not None:\n",
    "        curr_mask = mask.isel(k=np.arange(25,len(depth_coords)))\n",
    "        plot_mask(horiz_coords,depth_coords[k_split:],curr_mask,ax=curr_ax,color=(0,0,0))\n",
    "    curr_ax.set_ylim(curr_ax.get_ylim()[::-1])\n",
    "    curr_ax.set_ylabel('Depth [m]')\n",
    "    clim_1 = curr_plot_1.get_clim()\n",
    "    \n",
    "    # create shared colorbar for 2 subplots\n",
    "    new_clim = [np.fmin(clim_0[0],clim_1[0]),np.fmax(clim_0[1],clim_1[1])]\n",
    "    curr_plot_0.set_clim(new_clim)\n",
    "    curr_plot_1.set_clim(new_clim)\n",
    "    fig.colorbar(curr_plot_1,ax=axs[:])\n",
    "    return fig,axs\n",
    "\n",
    "# function to scale colormap, coordinating among multiple plots\n",
    "def cmap_zerocent_scale_multiplots(plot_objs,scale_factor):\n",
    "    \"\"\"\n",
    "    Center colormap at zero and scale relative to existing |maximum| value across multiple plots, \n",
    "    given plot objects (as list) and scale_factor, a number of type float.\n",
    "    Returns new colormap limits as new_clim.\n",
    "    \"\"\"\n",
    "    clim_plots = np.empty((len(plot_objs),2))\n",
    "    for count,curr_obj in enumerate(plot_objs):\n",
    "        clim_plots[count,:] = curr_obj.get_clim()\n",
    "    new_clim = (scale_factor*np.nanmax(np.abs(clim_plots)))*np.array([-1,1])\n",
    "    for curr_obj in plot_objs:\n",
    "        curr_obj.set_clim(new_clim)\n",
    "    return new_clim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8696428a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot density data along target latitude\n",
    "\n",
    "# retrieve density field\n",
    "densanom = ds_dens.RHOAnoma\n",
    "rhoConst = 1029.\n",
    "dens = rhoConst + densanom\n",
    "\n",
    "# load land mask (a boolean array) from grid parameters\n",
    "land_mask = ~ds_grid.maskC\n",
    "\n",
    "# plot density along latitude line\n",
    "tile_plot = 10\n",
    "i_plot = target_i\n",
    "# create dictionaries to pass as indices to xarray isel\n",
    "isel_dict = dict({'tile':tile_plot,'i':i_plot})\n",
    "isel_dict_ig = dict({'tile':tile_plot,'i_g':i_plot})\n",
    "\n",
    "plt.rcParams[\"font.size\"] = 12        # set default font size for plots in this tutorial\n",
    "\n",
    "# make figure\n",
    "curr_mask = land_mask.isel(isel_dict).squeeze()\n",
    "fig,axs = depth_two_subplots(ds_grid.XG.isel(isel_dict_ig),-ds_grid.Zl,\\\n",
    "                             dens.isel(isel_dict).squeeze(),\\\n",
    "                             k_split=25,cmap='seismic_r',mask=curr_mask)\n",
    "                            # '_r' at the end of a colormap name reverses the colormap\n",
    "axs[0].set_title('Density [kg m$^{-3}$] along 26 N latitude')\n",
    "axs[1].set_xlabel('Longitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f07ce4d1",
   "metadata": {},
   "source": [
    "### Density anomalies at depth\n",
    "\n",
    "Notice that in the plots above the density is primarily a function of depth (i.e. pressure). However, thermal wind balance involves horizontal (not vertical) density gradients, and so the density structure that drives the ocean circulation is largely obscured in these plots. One way to look at the horizontal variation of density is to remove a mean density at each depth level; here's what this looks like if we compute the means using only grid cells along this transect:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4b67fabe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tile_plot = 10\n",
    "i_plot = target_i\n",
    "isel_dict = dict({'tile':tile_plot,'i':i_plot})\n",
    "isel_dict_ig = dict({'tile':tile_plot,'i_g':i_plot})\n",
    "\n",
    "# compute horizontal density means at each depth level\n",
    "curr_mask = (~np.isnan((ds_grid.rA*dens)))\n",
    "dens_mean_atdepth = ((curr_mask*ds_grid.rA*dens).isel(isel_dict).sum(\"j\"))\\\n",
    "                    /((curr_mask*ds_grid.rA).isel(isel_dict).sum(\"j\"))\n",
    "\n",
    "# anomalies from density means at depth\n",
    "dens_anom_atdepth = dens.isel(isel_dict) - dens_mean_atdepth\n",
    "\n",
    "\n",
    "# make figure\n",
    "curr_mask = land_mask.isel(isel_dict).squeeze()\n",
    "fig,axs = depth_two_subplots(ds_grid.XG.isel(isel_dict_ig),-ds_grid.Zl,\\\n",
    "                             dens_anom_atdepth.squeeze(),\\\n",
    "                             k_split=25,cmap='seismic_r',mask=curr_mask)\n",
    "axs[0].set_title('Density minus transect horiz mean [kg m$^{-3}$],\\n along 26 N latitude')\n",
    "axs[1].set_xlabel('Longitude')\n",
    "plot_objs = [axs[0].get_children()[0],axs[1].get_children()[0]]\n",
    "cmap_zerocent_scale_multiplots(plot_objs,0.8)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ed43c21",
   "metadata": {},
   "source": [
    "and if we instead remove the global mean at each depth level:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "39ae8fe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute horizontal density means at each depth level\n",
    "curr_mask = (~np.isnan((ds_grid.rA*dens)))\n",
    "dens_mean_atdepth = (((curr_mask*ds_grid.rA*dens).sum(\"tile\")).sum(\"j\")).sum(\"i\")\\\n",
    "                    /((((curr_mask*ds_grid.rA).sum(\"tile\")).sum(\"j\")).sum(\"i\"))\n",
    "\n",
    "# anomalies from density means at depth\n",
    "dens_anom_atdepth = dens.isel(isel_dict) - dens_mean_atdepth\n",
    "\n",
    "# make figure\n",
    "curr_mask = land_mask.isel(isel_dict).squeeze()\n",
    "fig,axs = depth_two_subplots(ds_grid.XG.isel(isel_dict_ig),-ds_grid.Zl,\\\n",
    "                             dens_anom_atdepth.squeeze(),\\\n",
    "                             k_split=25,cmap='seismic_r',mask=curr_mask)\n",
    "axs[0].set_title('Density minus global horiz mean [kg m$^{-3}$],\\n along 26 N latitude')\n",
    "axs[1].set_xlabel('Longitude')\n",
    "plot_objs = [axs[0].get_children()[0],axs[1].get_children()[0]]\n",
    "cmap_zerocent_scale_multiplots(plot_objs,0.8)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e38004d5",
   "metadata": {},
   "source": [
    "### Plotting along a line of latitude (multiple tiles)\n",
    "\n",
    "All the plots above consider a transect in a single tile, but ocean basins generally span multiple tiles of the llc90 grid architecture. In the code below, we define functions to map transects following a line of latitude, across multiple tiles if need be. Then we apply the function to plot density minus horizontal means along the Atlantic 26 N transect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ca82eea9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # define some functions (these are also in the ecco_po_tutorials.py module)\n",
    "\n",
    "# define function to take modulus 360, output in a given range\n",
    "def mod_360_range(n,low_bound):\n",
    "    \"Compute n mod 360; output is in the range [n,n+360)\"\n",
    "    out = ((n - low_bound) % 360) + low_bound\n",
    "    return out\n",
    "\n",
    "# define function that searches for and outputs grid indices along line of latitude\n",
    "def llc_grid_idx_along_lat(lat_transect,lon_bnds):\n",
    "    \"\"\"\n",
    "    Finds grid indices along a given line of latitude.\n",
    "    Input parameters:\n",
    "    - lat_transect: line of latitude to plot along, float\n",
    "    - lon_bnds: 2 elements specifying western and eastern longitude bounds of transect, list or numpy array\n",
    "                The difference between the bounds must be >0 and <=360\n",
    "    Outputs:\n",
    "    - idx_along_lat: grid indices along line of latitude, dict containing 'tile','j','i' as keys\n",
    "    - XC_along_lat: longitude of grid cell centers, numpy array\n",
    "    - XG_along_lat: longitude of western grid cell edges, numpy array\n",
    "    \"\"\"\n",
    "    \n",
    "    # identify indices of grid cells along latitude transect\n",
    "    mask_along_lat = np.logical_and((ds_grid.YC_bnds > lat_transect).sum(\"nb\") > 0,\\\n",
    "                                    (ds_grid.YC_bnds > lat_transect).sum(\"nb\") < 4)\n",
    "    mask_in_lon_bnds = np.logical_and((mod_360_range(ds_grid.XC,lon_bnds[1]) > mod_360_range(lon_bnds[0],lon_bnds[1])),\\\n",
    "                                      (mod_360_range(ds_grid.XC,lon_bnds[0]) < mod_360_range(lon_bnds[1],lon_bnds[0] + 1.e-5)))\n",
    "    mask_along_lat = np.logical_and(mask_along_lat,mask_in_lon_bnds)\n",
    "    idx_along_lat = mask_along_lat.values.nonzero()\n",
    "    # create longitude arrays along transect\n",
    "    XC_along_lat = np.zeros((len(idx_along_lat[0]),))\n",
    "    XG_along_lat = np.zeros((len(idx_along_lat[0]),))\n",
    "    # for loop through indices along transect, using zip to iterate through three spatial indices\n",
    "    for count,(tile_idx,j_idx,i_idx) in enumerate(zip(idx_along_lat[0],idx_along_lat[1],idx_along_lat[2])):\n",
    "        curr_XC = mod_360_range(ds_grid.XC[tile_idx,j_idx,i_idx],lon_bnds[0])\n",
    "        XC_along_lat[count] = curr_XC\n",
    "        XG_along_lat[count] = np.min(mod_360_range(ds_grid.XC_bnds[tile_idx,j_idx,i_idx,:],curr_XC - 180))\n",
    "    # sort grid cells in order of increasing longitude\n",
    "    idx_sorted = np.argsort(XC_along_lat)\n",
    "    XC_along_lat = XC_along_lat[idx_sorted]\n",
    "    XG_along_lat = XG_along_lat[idx_sorted]\n",
    "    tile_idx_sorted = idx_along_lat[0][idx_sorted]\n",
    "    j_idx_sorted = idx_along_lat[1][idx_sorted]\n",
    "    i_idx_sorted = idx_along_lat[2][idx_sorted]\n",
    "    idx_along_lat = {'tile':tile_idx_sorted,'j':j_idx_sorted,'i':i_idx_sorted}\n",
    "    return idx_along_lat,XC_along_lat,XG_along_lat\n",
    "\n",
    "# define function to retrieve data along latitude transect, given grid indices\n",
    "def data_along_lat(data_in,idx_along_lat,XC_along_lat,XG_along_lat):\n",
    "    \"\"\"\n",
    "    Creates xarray DataArray along latitude transect, given grid indices\n",
    "    Input parameters:\n",
    "    - data_in: data to plot along latitude, xarray DataArray or numpy array\n",
    "            must have 4 or 5 dimensions, 'time','k','tile','j','i', 'time' is optional\n",
    "    - XC_along_lat: longitudes of grid cell centers, numpy array\n",
    "    - XG_along_lat: longitudes of western grid cell edges, numpy array\n",
    "    Outputs:\n",
    "    - data_xrarray: data along latitude transect, xarray DataArray\n",
    "    \"\"\"\n",
    "    \n",
    "    # retrieve grid indices along transect\n",
    "    tile_idx_along = idx_along_lat['tile']\n",
    "    j_idx_along = idx_along_lat['j']\n",
    "    i_idx_along = idx_along_lat['i']\n",
    "    \n",
    "    # construct DataArray along transect\n",
    "    data_along_lat = np.empty(data_in.shape[:-3] + (len(idx_along_lat['i']),))\n",
    "    data_along_lat.fill(np.nan)\n",
    "    for count,(tile_idx,j_idx,i_idx) in enumerate(zip(tile_idx_along,j_idx_along,i_idx_along)):\n",
    "        if len(data_in.shape) == 4:\n",
    "            data_along_lat[:,count] = data_in[:,tile_idx,j_idx,i_idx]\n",
    "        elif len(data_in.shape) == 5:\n",
    "            data_along_lat[:,:,count] = data_in[:,:,tile_idx,j_idx,i_idx]\n",
    "    if len(data_in.shape) == 4:    # no time dimension\n",
    "        data_xrarray = xr.DataArray(\n",
    "                    data=data_along_lat,\n",
    "                    dims=[\"k\",\"lon\"],\n",
    "                    coords=dict(\n",
    "                        Z=([\"k\"],ds_grid.Z.data),\n",
    "                        lon=([\"lon\"],XC_along_lat),\n",
    "                        lonW=([\"lon\"],XG_along_lat),\n",
    "                        lat=lat_transect,\n",
    "                    ),\n",
    "        )\n",
    "    elif len(data_in.shape) == 5:    # include time dimension\n",
    "        try:\n",
    "            time_coord = data_in.time.data\n",
    "        except:\n",
    "            time_coord = np.nan\n",
    "        data_xrarray = xr.DataArray(\n",
    "                    data=data_along_lat,\n",
    "                    dims=[\"time\",\"k\",\"lon\"],\n",
    "                    coords=dict(\n",
    "                        time=([\"time\"],time_coord),\n",
    "                        Z=([\"k\"],ds_grid.Z.data),\n",
    "                        lon=([\"lon\"],XC_along_lat),\n",
    "                        lonW=([\"lon\"],XG_along_lat),\n",
    "                        lat=lat_transect,\n",
    "                    ),\n",
    "        )\n",
    "    \n",
    "    return data_xrarray    \n",
    "    \n",
    "    \n",
    "# define function to extract data along line of latitude\n",
    "def lon_depth_along_lat(lat_transect,lon_bnds,data_in):\n",
    "    \"\"\"\n",
    "    Function to extract data along line of latitude.\n",
    "    Input parameters:\n",
    "    - lat_transect: line of latitude to plot along, float\n",
    "    - lon_bnds: 2 elements specifying western and eastern longitude bounds of transect, list or numpy array\n",
    "    - data_in: data, numpy array or xarray DataArray\n",
    "    Outputs:\n",
    "    - XC_along_lat: longitude of grid cell centers, numpy array\n",
    "    - XG_along_lat: longitude of western grid cell edges, numpy array\n",
    "    - data_xrarray: data along latitude transect, xarray DataArray\n",
    "    \"\"\"\n",
    "    \n",
    "    # identify indices of grid cells along latitude transect\n",
    "    idx_along_lat,XC_along_lat,XG_along_lat = llc_grid_idx_along_lat(lat_transect,lon_bnds)\n",
    "    \n",
    "    # construct DataArray along transect\n",
    "    data_xrarray = data_along_lat(data_in,idx_along_lat,XC_along_lat,XG_along_lat)\n",
    "    \n",
    "    return XC_along_lat,XG_along_lat,data_xrarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f57f127f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create data array along line of latitude\n",
    "lat_transect = 26.\n",
    "Atl_W_bound = -82.\n",
    "Atl_E_bound = -14.\n",
    "lon_bnds = [Atl_W_bound,Atl_E_bound]\n",
    "XC_transect,XG_transect,mask_transect = lon_depth_along_lat(lat_transect,lon_bnds,land_mask)\n",
    "XC_transect,XG_transect,dens_transect = lon_depth_along_lat(lat_transect,lon_bnds,dens)\n",
    "\n",
    "# anomalies from density means at depth\n",
    "dens_anom_transect = dens_transect - np.expand_dims(dens_mean_atdepth,axis=-1)\n",
    "\n",
    "# make figure\n",
    "fig,axs = depth_two_subplots(XG_transect,-ds_grid.Zl,\\\n",
    "                             dens_anom_transect.squeeze(),\\\n",
    "                             k_split=25,cmap='seismic_r',mask=mask_transect)\n",
    "axs[0].set_title('Density minus global horiz mean [kg m$^{-3}$],\\n along 26 N latitude')\n",
    "axs[1].set_xlabel('Longitude')\n",
    "plot_objs = [axs[0].get_children()[0],axs[1].get_children()[0]]\n",
    "cmap_zerocent_scale_multiplots(plot_objs,0.8)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b230253c",
   "metadata": {},
   "source": [
    "## Computing thermal wind balance\n",
    "\n",
    "### Thermal wind balance across Atlantic (26 N)\n",
    "\n",
    "Now let's compute horizontal density gradients (times $g/f$) and compare with vertical velocity gradients---i.e., thermal wind balance. This calculation is complicated by the fact that density, $u$ and $v$ all start out at different positions on each grid cell, and that we take differences in both horizontal and vertical directions. To enable this to be done so that both sides of the equations end up on the same points in the grid, the calculations are done in the following order:\n",
    "\n",
    "* Density at center of grid cells (i,j,k) --horiz difference-->\n",
    "    * Horizontal density gradients at edge of grid cells (i_g,j,k) or (i,j_g,k) --horiz interpolate-->\n",
    "        * Interpolated horizontal density gradients (i,j,k)\n",
    "\n",
    "\n",
    "* $u$,$v$ at edge of grid cells (i_g,j,k) or (i,j_g,k) --horiz interpolate-->\n",
    "    * Centered $u$,$v$ (i,j,k) --vert difference-->\n",
    "        * Vertical $u$,$v$ gradients (i,j,k_l) --vert interpolate-->\n",
    "            * Interpolated $u$,$v$ gradients (i,j,k)\n",
    "\n",
    "Then we rotate velocities and gradients from model axes to geographical (zonal and meridional) axes. Finally, we apply the *lon_depth_along_lat* function used above to look at the thermal wind balance across the Atlantic basin.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "13477c6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get xgcm Grid object for ECCO LLC90 grid\n",
    "xgcm_grid = ecco.get_llc_grid(ds_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3f85d074",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/conda/envs/jupyter/lib/python3.11/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg\n"
     ]
    }
   ],
   "source": [
    "g = 9.81\n",
    "# compute f from latitude of grid cell centers\n",
    "lat = ds_grid.YC\n",
    "Omega = (2*np.pi)/86164\n",
    "lat_rad = (np.pi/180)*lat    # convert latitude from degrees to radians\n",
    "# expand dimensions so that f broadcasts correctly across DataArrays\n",
    "f = np.expand_dims(2*Omega*np.sin(lat_rad),axis=(0,1))\n",
    "\n",
    "# compute derivatives of density in x and y\n",
    "d_rho_dx = (xgcm_grid.diff(dens,axis=\"X\",boundary='extend'))/ds_grid.dxC\n",
    "d_rho_dy = (xgcm_grid.diff(dens,axis=\"Y\",boundary='extend'))/ds_grid.dyC\n",
    "# interpolate (vector) gradient values to center of grid cells\n",
    "import warnings\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")    # use this to ignore future warnings caused by interp_2d_vector function\n",
    "    rho_grads_interp = xgcm_grid.interp_2d_vector({\"X\":d_rho_dx,\"Y\":d_rho_dy},boundary='extend')\n",
    "d_rho_dx = rho_grads_interp['X']\n",
    "d_rho_dy = rho_grads_interp['Y']\n",
    "d_rho_dx.name = 'drho_dx'\n",
    "d_rho_dy.name = 'drho_dy'\n",
    "\n",
    "# rotate from model to geographical (lon-lat) axes\n",
    "CS = np.expand_dims(ds_grid.CS,axis=(0,1))\n",
    "SN = np.expand_dims(ds_grid.SN,axis=(0,1))\n",
    "d_rho_zonal = (CS*d_rho_dx) - (SN*d_rho_dy)\n",
    "d_rho_merid = (SN*d_rho_dx) + (CS*d_rho_dy)\n",
    "\n",
    "# right-hand side of thermal wind balance equations\n",
    "therm_wind_RHS_1 = -(g/(f*dens))*d_rho_zonal\n",
    "therm_wind_RHS_2 = (g/(f*dens))*d_rho_merid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ce503a83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load velocity dataset\n",
    "download_dir = join(download_root_dir,vel_monthly_shortname)\n",
    "if incloud_access == True:\n",
    "    curr_vel_file = files_dict[vel_monthly_shortname]\n",
    "else:\n",
    "    curr_vel_file = list(glob.glob(join(download_dir,'*2000-01*.nc')))\n",
    "ds_vel = xr.open_dataset(curr_vel_file[0])\n",
    "\n",
    "# interpolate velocities to center of grid cells\n",
    "ds_vel.UVEL.values[np.isnan(ds_vel.UVEL.values)] = 0\n",
    "ds_vel.VVEL.values[np.isnan(ds_vel.VVEL.values)] = 0\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    vel_interp = xgcm_grid.interp_2d_vector({'X':ds_vel.UVEL,'Y':ds_vel.VVEL},boundary='extend')\n",
    "u = vel_interp['X']\n",
    "v = vel_interp['Y']\n",
    "\n",
    "# vertical derivatives of velocity \n",
    "# (negative sign after = because z increases upward, while k indices decrease upward)\n",
    "du_dz_gridedge = -(u.diff(\"k\").values)/np.expand_dims(ds_grid.drC[1:-1].values,axis=(0,2,3,4))\n",
    "dv_dz_gridedge = -(v.diff(\"k\").values)/np.expand_dims(ds_grid.drC[1:-1].values,axis=(0,2,3,4))\n",
    "\n",
    "# vertical interpolation of du/dz, dv/dz\n",
    "du_dz = np.empty(u.shape)\n",
    "du_dz.fill(np.nan)    # fill empty numpy array with NaNs\n",
    "dv_dz = np.empty(v.shape)\n",
    "dv_dz.fill(np.nan)\n",
    "du_dz[:,1:-1,:,:,:] = du_dz_gridedge[:,1:,:,:,:] - (np.diff(du_dz_gridedge,axis=1)/2)\n",
    "dv_dz[:,1:-1,:,:,:] = dv_dz_gridedge[:,1:,:,:,:] - (np.diff(dv_dz_gridedge,axis=1)/2)\n",
    "# create xarray DataArrays\n",
    "du_dz_array = xr.DataArray(\n",
    "                data=du_dz,\n",
    "                dims=[\"time\",\"k\",\"tile\",\"j\",\"i\"],\n",
    "                coords=u.coords,\n",
    ")\n",
    "dv_dz_array = xr.DataArray(\n",
    "                data=dv_dz,\n",
    "                dims=[\"time\",\"k\",\"tile\",\"j\",\"i\"],\n",
    "                coords=v.coords,\n",
    ")\n",
    "\n",
    "# rotate from model to geographical (lon-lat) axes\n",
    "du_dz_zonal = (CS*du_dz_array) - (SN*dv_dz_array)\n",
    "dv_dz_merid = (SN*du_dz_array) + (CS*dv_dz_array)\n",
    "\n",
    "# left-hand side of thermal wind balance equations\n",
    "therm_wind_LHS_1 = dv_dz_merid\n",
    "therm_wind_LHS_2 = du_dz_zonal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "33a2a0a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate DataArrays along line of latitude\n",
    "lat_transect = 26.\n",
    "Atl_W_bound = -82.\n",
    "Atl_E_bound = -14.\n",
    "lon_bnds = [Atl_W_bound,Atl_E_bound]\n",
    "\n",
    "# use llc_grid_idx_along_lat and data_along_lat functions separately (runs faster)\n",
    "\n",
    "idx_along_lat,XC_transect,XG_transect = llc_grid_idx_along_lat(lat_transect,lon_bnds)\n",
    "\n",
    "mask_transect = data_along_lat(land_mask,idx_along_lat,XC_transect,XG_transect)\n",
    "tw_LHS_1_transect = data_along_lat(therm_wind_LHS_1,idx_along_lat,XC_transect,XG_transect)\n",
    "tw_LHS_2_transect = data_along_lat(therm_wind_LHS_2,idx_along_lat,XC_transect,XG_transect)\n",
    "tw_RHS_1_transect = data_along_lat(therm_wind_RHS_1,idx_along_lat,XC_transect,XG_transect)\n",
    "tw_RHS_2_transect = data_along_lat(therm_wind_RHS_2,idx_along_lat,XC_transect,XG_transect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0eca70ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make figure\n",
    "plt.rcParams[\"font.size\"] = 12   # change font size\n",
    "fig,axs = plt.subplots(4,2,figsize=(10,15))\n",
    "depth_two_subplots(XG_transect,-ds_grid.Zl,\\\n",
    "                     tw_LHS_1_transect.squeeze(),\\\n",
    "                     k_split=25,cmap='seismic',mask=mask_transect,\\\n",
    "                      fig=fig,axs=axs[:2,0])\n",
    "axs[0,0].set_title('dv/dz [s$^{-1}$] along 26 N latitude,\\n in lon-lat axes')\n",
    "\n",
    "depth_two_subplots(XG_transect,-ds_grid.Zl,\\\n",
    "                     tw_RHS_1_transect.squeeze(),\\\n",
    "                     k_split=25,cmap='seismic',mask=mask_transect,\\\n",
    "                      fig=fig,axs=axs[:2,1])\n",
    "axs[0,1].set_title('$-(g/f\\\\rho)(d\\\\rho/dx)$ [s$^{-1}$] along 26 N latitude,\\n in lon-lat axes')\n",
    "plot_objs = [axs[0,0].get_children()[0],axs[1,0].get_children()[0],\\\n",
    "             axs[0,1].get_children()[0],axs[1,1].get_children()[0]]\n",
    "cmap_zerocent_scale_multiplots(plot_objs,0.2)\n",
    "\n",
    "depth_two_subplots(XG_transect,-ds_grid.Zl,\\\n",
    "                     tw_LHS_2_transect.squeeze(),\\\n",
    "                     k_split=25,cmap='seismic',mask=mask_transect,\\\n",
    "                      fig=fig,axs=axs[2:,0])\n",
    "axs[2,0].set_title('du/dz [s$^{-1}$] along 26 N latitude')\n",
    "axs[3,0].set_xlabel('Longitude')\n",
    "\n",
    "depth_two_subplots(XG_transect,-ds_grid.Zl,\\\n",
    "                     tw_RHS_2_transect.squeeze(),\\\n",
    "                     k_split=25,cmap='seismic',mask=mask_transect,\\\n",
    "                      fig=fig,axs=axs[2:,1])\n",
    "axs[2,1].set_title('$(g/f\\\\rho)(d\\\\rho/dy)$ [s$^{-1}$] along 26 N latitude')\n",
    "axs[3,1].set_xlabel('Longitude')\n",
    "plot_objs = [axs[2,0].get_children()[0],axs[3,0].get_children()[0],\\\n",
    "             axs[2,1].get_children()[0],axs[3,1].get_children()[0]]\n",
    "cmap_zerocent_scale_multiplots(plot_objs,0.2)\n",
    "\n",
    "# remove colorbars from left column plots (to declutter figure)\n",
    "fig.get_children()[-4].remove()\n",
    "fig.get_children()[-2].remove()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "defdb51b",
   "metadata": {},
   "source": [
    "Note that the plots in the left and right columns are very similar at depths below ~100 meters, and very different in the top 100 meters. Why do you think this is?\n",
    "\n",
    "### Velocity reconstruction using thermal wind\n",
    "\n",
    "In early studies of the atmosphere and ocean, measuring in-situ velocities accurately was nearly impossible, but properties such as temperature, humidity, and salinity (that are closely related to density) could be more readily observed. Even in present-day observing systems such as atmospheric radiosondes and oceanic Argo floats, observing the density structure can tell us a lot about the movement of the fluid, because of the application of thermal wind balance.\n",
    "\n",
    "Given the thermal wind balance equations introduced earlier, integrating vertically between two depth levels $z = z_0$ and $z = z_1$ gives us\n",
    "\n",
    "$$\n",
    "v_g(z_1) - v_g(z_0) = -\\frac{g}{f}\\int_{z_0}^{z_1} \\frac{1}{\\rho}\\frac{\\partial{\\rho}}{\\partial{x}}\\, dz\n",
    "$$\n",
    "\n",
    "$$\n",
    "u_g(z_1) - u_g(z_0) = \\frac{g}{f}\\int_{z_0}^{z_1} \\frac{1}{\\rho}\\frac{\\partial{\\rho}}{\\partial{y}}\\, dz\n",
    "$$\n",
    "\n",
    "Since velocities in the deep ocean tend to be much smaller than those near the surface, a common technique used in oceanography has been to assume a *level of no motion*, i.e., let $u_g(z_0) = v_g(z_0) = 0$. Then upper ocean geostrophic velocities can be inferred from vertical profiles of density, spaced at regular intervals. Here we can assess the accuracy of this technique using ECCO v4r4 output.\n",
    "\n",
    "## Assessment\n",
    "\n",
    "### Thermal wind-based velocity reconstruction\n",
    "\n",
    "Let's integrate geostrophic velocity from a depth level where we assume the velocity is zero (even though we know from ECCO output that the velocity will not be exactly zero). In this example we'll take the level of no motion to be 3000 meters depth. Then we consider the result of this velocity reconstruction along the Atlantic 26 N transect we just considered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d41eae12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set level of no motion\n",
    "z_0 = -3000.\n",
    "\n",
    "# integrate (sum) RHS of thermal wind balance upward\n",
    "dist_z0_upper = ds_grid.Zl - z_0\n",
    "dist_z0_upper[ds_grid.Zl < z_0] = 0\n",
    "delta_upper = (-dist_z0_upper.diff(\"k_l\")).values\n",
    "delta_upper = np.concatenate((delta_upper,np.array([0,])))\n",
    "delta_upper = np.expand_dims(delta_upper,axis=(0,2,3,4))\n",
    "v_g_reconstr_upward = (((delta_upper*therm_wind_RHS_1)\\\n",
    "                       .sel(k=slice(None,None,-1)))\\\n",
    "                        .cumsum(dim=\"k\",skipna=True))\\\n",
    "                        .sel(k=slice(None,None,-1))    # slice object \"flips\" array along k dimension\n",
    "u_g_reconstr_upward = (((delta_upper*therm_wind_RHS_2)\\\n",
    "                       .sel(k=slice(None,None,-1)))\\\n",
    "                        .cumsum(dim=\"k\",skipna=True))\\\n",
    "                        .sel(k=slice(None,None,-1))    # slice object \"flips\" array along k dimension\n",
    "# interpolate DataArrays to center of tracer cells\n",
    "v_g_reconstr_upward_interp = v_g_reconstr_upward\n",
    "v_g_reconstr_upward_interp.isel(k=np.arange(0,len(ds_grid.k) - 1)).values = \\\n",
    "                            v_g_reconstr_upward.isel(k=np.arange(0,len(ds_grid.k) - 1))\\\n",
    "                            + ((v_g_reconstr_upward.diff(\"k\").values)/2)\n",
    "u_g_reconstr_upward_interp = u_g_reconstr_upward\n",
    "u_g_reconstr_upward_interp.isel(k=np.arange(0,len(ds_grid.k) - 1)).values = \\\n",
    "                            u_g_reconstr_upward.isel(k=np.arange(0,len(ds_grid.k) - 1))\\\n",
    "                            + ((u_g_reconstr_upward.diff(\"k\").values)/2)\n",
    "\n",
    "# integrate (sum) RHS of thermal wind balance downward\n",
    "dist_z0_lower = ds_grid.Zu - z_0\n",
    "dist_z0_lower[ds_grid.Zu > z_0] = 0\n",
    "delta_lower = (-dist_z0_lower.diff(\"k_u\")).values\n",
    "delta_lower = np.concatenate((np.array([0,]),delta_lower))\n",
    "delta_lower = np.expand_dims(delta_lower,axis=(0,2,3,4))\n",
    "v_g_reconstr_downward = (delta_lower*therm_wind_RHS_1)\\\n",
    "                       .cumsum(dim=\"k\",skipna=True)\n",
    "u_g_reconstr_downward = (delta_lower*therm_wind_RHS_2)\\\n",
    "                        .cumsum(dim=\"k\",skipna=True)\n",
    "v_g_reconstr_downward_interp = v_g_reconstr_downward.isel(k=np.arange(0,len(ds_grid.k) - 1))\\\n",
    "                                + ((v_g_reconstr_downward.diff(\"k\").values)/2)\n",
    "v_g_reconstr_downward_interp = v_g_reconstr_downward_interp.pad(k=(1,0),constant_value=0)\n",
    "u_g_reconstr_downward_interp = u_g_reconstr_downward.isel(k=np.arange(0,len(ds_grid.k) - 1))\\\n",
    "                                + ((u_g_reconstr_downward.diff(\"k\").values)/2)\n",
    "u_g_reconstr_downward_interp = u_g_reconstr_downward_interp.pad(k=(1,0),constant_value=0)\n",
    "\n",
    "# combine results from upward and downward integration\n",
    "v_g_reconstr = v_g_reconstr_upward_interp\n",
    "v_g_reconstr.values[:,v_g_reconstr.Z.values < z_0,:,:,:] = \\\n",
    "                                v_g_reconstr_downward_interp.values[:,v_g_reconstr.Z.values < z_0,:,:,:]\n",
    "u_g_reconstr = u_g_reconstr_upward_interp\n",
    "u_g_reconstr.values[:,u_g_reconstr.Z.values < z_0,:,:,:] = \\\n",
    "                                u_g_reconstr_downward_interp.values[:,u_g_reconstr.Z.values < z_0,:,:,:]\n",
    "\n",
    "# rotate (actual) velocities from model to geographical axes\n",
    "u_zonal = (CS*u) - (SN*v)\n",
    "v_merid = (SN*u) + (CS*v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8b014bed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate DataArrays along line of latitude\n",
    "lat_transect = 26.\n",
    "Atl_W_bound = -82.\n",
    "Atl_E_bound = -14.\n",
    "lon_bnds = [Atl_W_bound,Atl_E_bound]\n",
    "\n",
    "# # only need this line uncommented if new transect is specified above\n",
    "# idx_along_lat,XC_transect,XG_transect = llc_grid_idx_along_lat(lat_transect,lon_bnds)\n",
    "\n",
    "mask_transect = data_along_lat(land_mask,idx_along_lat,XC_transect,XG_transect)\n",
    "u_zonal_transect = data_along_lat(u_zonal,idx_along_lat,XC_transect,XG_transect)\n",
    "v_merid_transect = data_along_lat(v_merid,idx_along_lat,XC_transect,XG_transect)\n",
    "u_g_reconstr_transect = data_along_lat(u_g_reconstr,idx_along_lat,XC_transect,XG_transect)\n",
    "v_g_reconstr_transect = data_along_lat(v_g_reconstr,idx_along_lat,XC_transect,XG_transect)\n",
    "\n",
    "# make figure\n",
    "plt.rcParams[\"font.size\"] = 12   # change font size\n",
    "fig,axs = plt.subplots(4,2,figsize=(10,15))\n",
    "depth_two_subplots(XG_transect,-ds_grid.Zl,\\\n",
    "                     v_merid_transect.squeeze(),\\\n",
    "                     k_split=25,cmap='seismic',mask=mask_transect,\\\n",
    "                      fig=fig,axs=axs[:2,0])\n",
    "axs[0,0].set_title('Meridional v [m s$^{-1}$]\\n along 26 N latitude')\n",
    "\n",
    "depth_two_subplots(XG_transect,-ds_grid.Zl,\\\n",
    "                     v_g_reconstr_transect.squeeze(),\\\n",
    "                     k_split=25,cmap='seismic',mask=mask_transect,\\\n",
    "                      fig=fig,axs=axs[:2,1])\n",
    "axs[0,1].set_title('v reconstruction [m s$^{-1}$]\\n from z_0 = ' + str(int(-z_0)) + ' meters depth')\n",
    "plot_objs = [axs[0,0].get_children()[0],axs[1,0].get_children()[0],\\\n",
    "             axs[0,1].get_children()[0],axs[1,1].get_children()[0]]\n",
    "cmap_zerocent_scale_multiplots(plot_objs,0.5)\n",
    "\n",
    "depth_two_subplots(XG_transect,-ds_grid.Zl,\\\n",
    "                     u_zonal_transect.squeeze(),\\\n",
    "                     k_split=25,cmap='seismic',mask=mask_transect,\\\n",
    "                      fig=fig,axs=axs[2:,0])\n",
    "axs[2,0].set_title('Zonal u [m s$^{-1}$]')\n",
    "axs[3,0].set_xlabel('Longitude')\n",
    "\n",
    "depth_two_subplots(XG_transect,-ds_grid.Zl,\\\n",
    "                     u_g_reconstr_transect.squeeze(),\\\n",
    "                     k_split=25,cmap='seismic',mask=mask_transect,\\\n",
    "                      fig=fig,axs=axs[2:,1])\n",
    "axs[2,1].set_title('u reconstruction [m s$^{-1}$]')\n",
    "axs[3,1].set_xlabel('Longitude')\n",
    "plot_objs = [axs[2,0].get_children()[0],axs[3,0].get_children()[0],\\\n",
    "             axs[2,1].get_children()[0],axs[3,1].get_children()[0]]\n",
    "cmap_zerocent_scale_multiplots(plot_objs,0.5)\n",
    "\n",
    "# remove colorbars from left column plots (to declutter figure)\n",
    "fig.get_children()[-4].remove()\n",
    "fig.get_children()[-2].remove()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "433d9dc7",
   "metadata": {},
   "source": [
    "### Latitude and depth dependence\n",
    "\n",
    "Now as we did with geostrophic balance in the previous tutorial, we can compute the normalized difference over the global domain, sorted into bins by latitude and depth. **Note: the ```mean_weighted_binned``` function may take a few minutes to run over the global domain.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5a35775a",
   "metadata": {},
   "outputs": [],
   "source": [
    "u_diff = u_zonal - u_g_reconstr\n",
    "v_diff = v_merid - v_g_reconstr\n",
    "vel_diff_complex = u_diff + (1j*v_diff)    # in Python, imaginary number i is indicated by 1j\n",
    "vel_complex = u_zonal + (1j*v_merid)\n",
    "\n",
    "# normalize magnitude of difference vector by magnitude of actual velocity\n",
    "vel_diff_abs = np.abs(vel_diff_complex)\n",
    "vel_abs = np.abs(vel_complex)\n",
    "vel_diff_norm = vel_diff_abs/vel_abs\n",
    "\n",
    "# bins of latitude\n",
    "lat_bin_spacing = 2\n",
    "lat_bin_bounds = np.c_[-90:90:lat_bin_spacing] + np.array([[0,lat_bin_spacing]])\n",
    "lat_bin_centers = np.mean(lat_bin_bounds,axis=-1)\n",
    "\n",
    "# bins of depth\n",
    "depth_bin_bounds = -ds_grid.Z_bnds.values\n",
    "\n",
    "\n",
    "# apply \"small velocity\" mask as in geostrophic balance tutorial\n",
    "mask_threshold = 0.005    # mask out velocities <0.5 cm s-1\n",
    "mask_smallvel = (vel_abs < mask_threshold)\n",
    "curr_weighting = ((~mask_smallvel)*(ds_grid.maskC)*(ds_grid.rA)).values\n",
    "\n",
    "# create depth array that will broadcast correctly across horizontal dimensions\n",
    "depth_expand_dims = np.expand_dims(-ds_grid.Z,axis=(-3,-2,-1))\n",
    "\n",
    "# compute normalized differences\n",
    "diff_norm_lat = mean_weighted_binned(vel_diff_norm.values,curr_weighting,\\\n",
    "                                    ds_grid.YC.values,lat_bin_bounds)\n",
    "diff_norm_depth = mean_weighted_binned(vel_diff_norm.values,curr_weighting,\\\n",
    "                                    depth_expand_dims,depth_bin_bounds)\n",
    "\n",
    "# apply additional masks for depth (100-1000 m) and latitude (>5 deg from equator) ranges\n",
    "\n",
    "depth_mask = np.logical_and(depth_expand_dims > 100,\\\n",
    "                            depth_expand_dims < 1000)\n",
    "lat_mask = np.expand_dims((np.abs(ds_grid.YC) > 5),axis=0)\n",
    "\n",
    "\n",
    "# compute norm diff with additional masks\n",
    "\n",
    "curr_weighting = (depth_mask*(~mask_smallvel)*(ds_grid.maskC)*(ds_grid.rA)).values\n",
    "diff_norm_lat_moremask = mean_weighted_binned(vel_diff_norm.values,curr_weighting,\\\n",
    "                                                ds_grid.YC.values,lat_bin_bounds)\n",
    "\n",
    "curr_weighting = (lat_mask*(~mask_smallvel)*(ds_grid.maskC)*(ds_grid.rA)).values\n",
    "diff_norm_depth_moremask = mean_weighted_binned(vel_diff_norm.values,curr_weighting,\\\n",
    "                                                depth_expand_dims,depth_bin_bounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5a571510",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load norm diff from previous geostrophic balance tutorial\n",
    "# (if you don't have this file run that tutorial,\n",
    "# or else comment out the lines containing geostr_file below)\n",
    "geostr_file = np.load('diff_norm_geostr_bal.npz')\n",
    "\n",
    "# plot normalized RMS difference, alongside values for geostrophic balance\n",
    "fig,axs = plt.subplots(2,2,figsize=(15,14))\n",
    "curr_ax = axs[0,0]\n",
    "curr_ax.plot(lat_bin_centers,geostr_file['diff_norm_lat'],color='blue',label='Geostrophy')\n",
    "curr_ax.plot(lat_bin_centers,diff_norm_lat,color='red',label='Thermal wind')\n",
    "curr_ax.set_ylim(0,1)\n",
    "curr_ax.axhline(y=0.1,color='black',lw=2)\n",
    "curr_ax.axvline(x=0,color='black',lw=1)\n",
    "curr_ax.set_xlabel('Latitude')\n",
    "curr_ax.set_ylabel('Normalized diff')\n",
    "curr_ax.set_title('Normalized diff variation with latitude\\n z_0 = ' + str(int(-z_0)) + ' meters depth')\n",
    "curr_ax.legend()\n",
    "curr_ax = axs[0,1]\n",
    "curr_ax.plot(geostr_file['diff_norm_depth'],-ds_grid.Z,color='blue',label='Geostrophy')\n",
    "curr_ax.plot(diff_norm_depth,-ds_grid.Z,color='red',label='Thermal wind')\n",
    "# Note: in older versions of Matplotlib\n",
    "# may need to use linthreshy instead of linthresh\n",
    "curr_ax.set_yscale('symlog',linthresh=100)\n",
    "curr_ax.invert_yaxis()\n",
    "curr_ax.axvline(x=0.1,color='black',lw=2)\n",
    "curr_ax.axhline(y=-z_0,color='black',lw=1)\n",
    "curr_ax.text(0.02,-0.92*z_0,'z_0')\n",
    "curr_ax.set_yticks([0,50,100,500,1000,5000])\n",
    "curr_ax.set_yticklabels(['0','50','100','500','1000','5000'])\n",
    "curr_ax.set_xlim(0,1)\n",
    "curr_ax.set_xlabel('Normalized diff')\n",
    "curr_ax.set_ylabel('Depth [m]')\n",
    "curr_ax.set_title('Normalized diff variation with depth\\n z_0 = ' + str(int(-z_0)) + ' meters depth')\n",
    "curr_ax.legend()\n",
    "curr_ax = axs[1,0]\n",
    "curr_ax.plot(lat_bin_centers,geostr_file['diff_norm_lat_moremask'],color='blue',label='Geostrophy')\n",
    "curr_ax.plot(lat_bin_centers,diff_norm_lat_moremask,color='red',label='Thermal wind')\n",
    "curr_ax.set_ylim(0,1)\n",
    "curr_ax.axhline(y=0.1,color='black',lw=2)\n",
    "curr_ax.axvline(x=0,color='black',lw=1)\n",
    "curr_ax.set_xlabel('Latitude')\n",
    "curr_ax.set_ylabel('Normalized diff')\n",
    "curr_ax.set_title('In 100-1000 m depth range only')\n",
    "curr_ax.legend()\n",
    "curr_ax = axs[1,1]\n",
    "curr_ax.plot(geostr_file['diff_norm_depth_moremask'],-ds_grid.Z,color='blue',label='Geostrophy')\n",
    "curr_ax.plot(diff_norm_depth_moremask,-ds_grid.Z,color='red',label='Thermal wind')\n",
    "# Note: in older versions of Matplotlib\n",
    "# may need to use linthreshy instead of linthresh\n",
    "curr_ax.set_yscale('symlog',linthresh=100)\n",
    "curr_ax.invert_yaxis()\n",
    "curr_ax.axvline(x=0.1,color='black',lw=2)\n",
    "curr_ax.axhline(y=-z_0,color='black',lw=1)\n",
    "curr_ax.text(0.02,-0.92*z_0,'z_0')\n",
    "curr_ax.set_yticks([0,50,100,500,1000,5000])\n",
    "curr_ax.set_yticklabels(['0','50','100','500','1000','5000'])\n",
    "curr_ax.set_xlim(0,1)\n",
    "curr_ax.set_xlabel('Normalized diff')\n",
    "curr_ax.set_ylabel('Depth [m]')\n",
    "curr_ax.set_title('>5 degrees from equator only')\n",
    "curr_ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "Comparing the geostrophy and thermal wind normalized differences provides some insight into the challenges of reconstructing velocity fields using thermal wind. Geostrophic balance is a more accurate predictor of velocity than thermal wind, since thermal wind assumes: \n",
    "\n",
    "* geostrophic balance\n",
    "* hydrostatic balance (not a factor here since hydrostatic balance is actually assumed in the model code itself)\n",
    "* a level of no motion (a potentially more problematic assumption, especially close to $z_0$).\n",
    "\n",
    "The plots above also illustrate why satellite observations of sea surface height are so valuable to oceanographers. Prior to reliable measurements of sea surface height, ocean velocity fields were typically reconstructed using density profiles and thermal wind. When sea surface height data are *combined* with density profiles, we can compute subsurface pressure gradients, and then obtain velocity estimates using only geostrophy, which is much more accurate than having to rely on thermal wind.\n",
    "\n",
    "## Exercises\n",
    "\n",
    "1. Try out different values of $z_0$ (i.e., level of no motion) to use in the velocity reconstructions. How does this affect the normalized differences in the plots above? Which $z_0$ values permit the most accurate reconstructions (lowest normalized difference) at 100 meters depth? (For bonus points, write some code to determine the optimal $z_0$ value that minimizes normalized difference at 100 m depth, rather than using trial and error.)\n",
    "\n",
    "1. Adapt the ```lon_depth_along_lat``` function (and the two functions it calls: ```llc_grid_idx_along_lat``` and ```data_along_lat```) to plot transects following lines of longitude instead of latitude. Use this to look at the application of thermal wind to reconstruct velocities through Drake Passage (~64 deg W, 55-65 deg S)."
   ]
  },
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   "cell_type": "markdown",
   "id": "8e89e099",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "Gill, A.E. (1982). ***Atmosphere-Ocean Dynamics***. Academic Press, Elsevier.\n",
    "\n",
    "Kundu, P.K. and Cohen, I.M. (2008). ***Fluid Mechanics*** (4th ed.). Elsevier.\n",
    "\n",
    "Vallis, G.K. (2006). ***Atmospheric and Oceanic Fluid Dynamics***. Cambridge University Press."
   ]
  }
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